Enhancing link prediction in dynamic social networks: a novel algorithm integrating global and local topological structures

Shambhu Kumar, Arti Jain and Dinesh C.S. Bisht

International Journal of Data Mining, Modelling and Management, 2025, vol. 17, issue 1, 26-53

Abstract: The link prediction problem has gained significant importance due to the emergence of many social networks. Existing link prediction algorithms in social networks often prioritise local or global attributes, yielding satisfactory performance on specific network types but with limitations like reduced accuracy or higher computational burden. This paper presents a novel link prediction approach that integrates global and local topological structures, assessing node similarity through a similarity index formula between two node pairs that is based on three key features: the number of common neighbours between nodes with some penalty factor introduced for each common node, node influence, and the shortest path distance between unconnected nodes. Evaluation using AUC has been performed against seven datasets and demonstrates significant improvement over baseline and state-of-the-art methods, enhancing accuracy by 30% and 6.75%. This highlights the efficacy of integrating global and local features for more accurate link prediction.

Keywords: social network; link prediction; common neighbour; similarity measure; degree centrality; node distance. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=144611 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:17:y:2025:i:1:p:26-53

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().